Metadata-Version: 2.1
Name: pyevals
Version: 1.1
Summary: A simple Python Package for Model Evalutaion
Home-page: https://github.com/Anand-gokul/pyevals
Author: Gokul and Anand
Author-email: adsp.tsgkr@gmail.com
License: UNKNOWN
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
Requires-Dist: xgboost
Requires-Dist: sklearn
Requires-Dist: numpy
Requires-Dist: pandas

<!-- PROJECT SHIELDS -->
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<!-- TABLE OF CONTENTS -->
## Table of Contents


* [About the Project](#about-the-project)
* [Installation](#installation)
* [Usage](#usage)
* [Future Work](#Futurework)
* [Contact](#contact)


<!-- ABOUT THE PROJECT -->
## About The Project


A very elegant and simple library to evaluate models.

This module will evaulate the Classification problems and Regression problems with 13 and 7 algorithms respectively. 

The Classification algorithms are KNN,LogisticRegression,DecisionTreeClassifier, RandomForestClassifier, SupportVectorClassifier, QuadraticDiscriminantSnalysis, SGDClassifier, XgBoost,AdaBoost, CalibratedClassifier, MultinomialNB, BernoulliNB, GaussianNB.

The Regression algorithms are LinearRegression, PolynomialRegression, RidgeRegression, LassoRegression, SupportVectorRegressor, GradientBoostingRegression, PLSRegression.

We also have implmented the Adjusted R Squared method as the Regression Metric Evaluation.

In Classification , Highest Accuracy is Highlighted in Yellow colour.

In Regression Model , Least Error is Highlighted in Yellow colour.


### Installation


1. Clone the repo
```sh
git clone https://github.com/Anand-gokul/pyevals.git
```

2. Install using pip or pip3
```commandline

pip3 install pyevals==1.0

(or)

pip install pyevals==1.0

```

<!-- USAGE EXAMPLES -->
## Usage


```python
import pyevals

# For Classification

Object = pyevals.build(x_train,x_test,y_train,y_test,'classification')
Object.evaluate()

# For Regression

Object = pyevals.build(x_train,x_test,y_train,y_test,'regression')
Object.evaluate()


```

### Future Work


In this version we are only providing the reports, but soon we will be adding plots too.We will also try to implement hyperparameter optimization techniques to get the better results. We will also try to implement other algorithms in classification and regression soon. 

<!-- CONTACT -->
## Contact


Sai Gokul Krishna Reddy Talla - [@Krish](https://www.linkedin.com/in/gokul-talla) - gokulkrishna.talla@gmail.com

Ananda Datta Sai Phanindra Tangirala - [@Anand](https://www.linkedin.com/in/ananda-datta-sai-phanindra-tangirala-62a4b5185) - tangiralaphanindra@gmail.com

Anirudh Palaparthi - [@anirudh8889](https://twitter.com/anirudh8889) - aniruddhapnbb@gmail.com

Project Link: [https://github.com/Anand-Gokul/pyevals](https://github.com/Anand-gokul/pyevals)


